聚类空间分类:用于遥感高光谱数据的快速k近邻分类

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI:10.1109/WARSD.2003.1295222

X. Jia, J. Richards

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引用次数: 8

摘要

本文提出了一种快速的k-近邻(k-NN)算法，该算法将k-NN与聚类空间数据表示相结合。该算法实现简单，可显著减少分类时间。使用Hyperion数据集进行的测试结果表明，简化对分类性能的影响很小，但效率大大提高。

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Cluster-space classification: a fast k-nearest neighbour classification for remote sensing hyperspectral data

In this paper a fast k-nearest neighbour (k-NN) algorithm is presented which combines k-NN with a cluster-space data representation. Implementation of the algorithm is easier and classification time can be significantly reduced. Results from tests carried out with a Hyperion data set demonstrate that the simplification has little effect on classification performance and yet efficiency is greatly improved.

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来源期刊

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003

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